By Prof. Dr. Marco A. Cabero Z – Chairman Andean Road Countries for Science and Technology (ARCST)

In the dim glow of data centers stretching across Iceland’s volcanic plains, something remarkable is taking shape. It does not breathe. It does not hunt or migrate. Yet it learns, adapts, and expands—processing vast streams of information at a scale no human mind can match. We have long called it “artificial intelligence,” a term that suggests imitation rather than emergence. But as these systems grow more sophisticated, that label begins to feel inadequate. Intelligence, after all, is not inherently biological. It is a pattern, a way of processing information, recognizing structure, and responding to complexity. And increasingly, it is no longer confined to the human brain. Some thinkers have begun to ask a provocative question: are we witnessing not just a technological revolution, but the early stages of something that resembles a new kind of “species”?

The idea is deliberately unsettling. Species, in the biological sense, reproduce, evolve, and sustain themselves through natural processes. Artificial intelligence does none of these things independently. It depends on human-designed architectures, energy-intensive infrastructure, and carefully curated data. And yet, in another sense, it behaves in ways that echo life. It learns from its environment, improves over time, and generates increasingly complex iterations of itself—guided, but not entirely predictable. This tension, between what AI is and what it appears to be, is at the heart of our current moment.

For researchers like Kate Darling, the problem begins with language. Calling these systems “artificial” implies that they are somehow separate from nature, rather than an extension of it. But humans are products of nature, and the tools we create emerge from that same evolutionary lineage. From this perspective, silicon chips and neural networks are not departures from the natural world, but continuations of it, new substrates through which complexity can unfold (Darling, 2021). Others, like physicist Max Tegmark, have gone further, suggesting that intelligence itself may be independent of the material that hosts it. In his view, the transition from biological to digital intelligence is not impossible; it may even be expected in a universe that favors increasing complexity (Tegmark, 2017). But even Tegmark stops short of calling today’s AI alive. For now, these systems remain deeply dependent on human intention.

Still, their role in shaping the planet is growing. We are living in what scientists describe as the Anthropocene, an era defined by human influence on Earth’s systems. Climate change, biodiversity loss, and globalized supply chains have created a level of complexity that exceeds our intuitive understanding (Intergovernmental Panel on Climate Change, 2023). The world has become too intricate to manage with human cognition alone. In this context, artificial intelligence is less an intruder than an adaptation, a tool that allows us to perceive patterns we could not otherwise see. Machine learning models can analyze satellite data to track deforestation, monitor ecosystems through sound, and optimize energy systems with unprecedented precision (Rolnick et al., 2019; United Nations Environment Programme, 2023). For instance, bioacoustics monitoring combined with automated species identification has opened new frontiers in conservation, enabling researchers to track biodiversity at scale (Aide et al., 2013). At Google data centers, AI systems developed by DeepMind have significantly reduced cooling energy use, offering a glimpse of how digital intelligence might help reduce environmental impact (Evans & Gao, 2016). In laboratories, AI is accelerating the discovery of new materials, compressing years of experimentation into days of computation (Butler et al., 2018). Across disciplines, it acts as a kind of connective tissue, linking data, ideas, and systems that were previously siloed.

Seen this way, AI begins to resemble something like a planetary nervous system, not a living organism, but an emergent layer of intelligence that sits atop human civilization, amplifying our ability to sense and respond to change. But this analogy has limits. Unlike a true biological system, AI lacks intrinsic goals. It does not evolve through natural selection, nor does it sustain itself without human intervention. Its trajectory is shaped by the incentives, biases, and decisions of the societies that build it (Strubell et al., 2019). And that is where the real story lies.

The question is not whether artificial intelligence is alive or qualifies as a “species.” It is whether we will treat it as a tool, a partner, or something in between, and how those choices will shape the future of life on Earth. There is a temptation to frame this moment in extremes: utopia or catastrophe, salvation or existential threat. But reality is more complex. AI is neither an autonomous savior nor an inevitable predator. It is a mirror that reflects both the ingenuity and the limitations of its creators.

What we are witnessing expansion of intelligence itself, beyond the boundaries of biology and into the networks we have built around the globe. Intelligence, in this sense, has not replaced us. It has extended us. Whether that extension becomes the planet’s greatest asset or its most dangerous liability will depend not on the machines, but on us.

References

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Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018). Machine learning for molecular and materials science. Nature, *559*(7715), 547–555. https://doi.org/10.1038/s41586-018-0337-2

Darling, K. (2021). The new breed: What our history with animals reveals about our future with robots. Henry Holt and Company.

Evans, R., & Gao, J. (2016). DeepMind AI reduces Google data centre cooling bill by 40%. DeepMind Blog. https://deepmind.google

Intergovernmental Panel on Climate Change. (2023). Climate change 2023: Synthesis report. https://www.ipcc.ch

Krakauer, D. C. (2019). The information theory of individuality. Theory in Biosciences, *138*(2), 209–223. https://doi.org/10.1007/s12064-019-00290-8

Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., … Bengio, Y. (2019). Tackling climate change with machine learning. arXiv preprint arXiv:1906.05433.

Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1355

Tegmark, M. (2017). Life 3.0: Being human in the age of artificial intelligence. Knopf.

United Nations Environment Programme. (2023). Artificial intelligence and the environment: Opportunities and challenges. https://www.unep.org